Automating Inventory at Stitch Fix

Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2y3TyB6.

Sally Langford talks about the use of ML within StitchFix’s inventory forecasting system, the architecture they have developed in-house, their use of Bayesian methods, and how this technique can be applied to similar examples of beta binomial regressions. Filmed at qconnewyork.com.

Sally Langford is a data scientist on the Merch Algorithms team at Stitch Fix, which is responsible for developing tools and methods for inventory optimization and product development.

3.
Presented at QCon New York
www.qconnewyork.com
Purpose of QCon
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Strategy
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Highlights
- attended by more than 12,000 delegates since 2007
- held in 9 cities worldwide

6.
- Tell us about your style, fit and price preferences.
- A personal stylist will curate five pieces for you.
How Stitch Fix works:

7.
- Tell us about your style, fit and price preferences.
- A personal stylist will curate five pieces for you.
- Try all the items on at home.
How Stitch Fix works:

8.
- Tell us about your style, fit and price preferences.
- A personal stylist will curate five pieces for you.
- Try all the items on at home.
- Give your stylist feedback on all items, then only pay for what you keep.
How Stitch Fix works:

9.
- Tell us about your style, fit and price preferences.
- A personal stylist will curate five pieces for you.
- Try all the items on at home.
- Give your stylist feedback on all items, then only pay for what you keep.
- Return the other items in envelope provided.
How Stitch Fix works:

11.
time
number of
units
order arrives
in warehouse
shirt is sent to
clients and is sold
plaid shirt

12.
time
order arrives
in warehouse
shirt is sent to
clients and is sold
plaid shirt
number of
units

13.
Inventory consumption of a style is proportional to;
- daily demand,
- clients for which the style is recommended,
- whether there are units in the warehouse,
- probability a stylist chooses to send the client this style,
- if the client buys the style.

14.
Inventory consumption of a style is proportional to;
- daily demand,
- clients for which the style is recommended,
- whether there are units in the warehouse,
- probability a stylist chooses to send the client this style,
- if the client buys the style.

15.
Inventory consumption of a style is proportional to;
- daily demand,
- clients for which the style is recommended,
- whether there are units in the warehouse,
- probability a stylist chooses to send the client this style,
- if the client buys the style.

16.
Ranked styles recommended for client - which will the stylist choose to send?

17.
Ranked styles recommended for client - which will the stylist choose to send?

18.
Ranked styles recommended for client - which will the stylist choose to send?

19.
Ranked styles recommended for client - which will the stylist choose to send?

20.
Ranked styles recommended for client - which will the stylist choose to send?

36.
Step 1: Use maximum likelihood to calculate α0
and β0
for the distribution of p in
groups of similar styles.
Step 2: After a period of time, update this prior for the number of times the new
style has been recommended for a client (n), and chosen to be sent (k).
Step 3: Calculate the mean and confidence interval of p from the resulting
distribution. This is used as the probability that the new style will be chosen to be
sent to a client.
Step 4: Repeat steps 2-3.

43.
number of
units
time
now
forecasted units
planned orders
new styles

44.
How do we use our inventory forecast model?
- When should we re-order inventory?
- How should we buy inventory by size?
- How should orders be separated into different warehouses?
- When should a style not be sent out anymore, in place of a new option?

45.
Metrics of success:
- Fraction of inventory out with clients compared to in the warehouse?
- How many styles are available to send to a client?
- ∆ in the beginning of month projected units.
- Cumulative units sold over time.

46.
Do you want to calculate the probability of success in a binomial process?
Not enough data?
Use Beta Binomial Regression for your cold start problem!